{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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o6VtwgNpUPHdPl5Ln76D9ZkVu3jeBXRkRcP7aVlFrNfA/P1Rw/SVUggi3B+Pk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4e8cb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 随机生成1000个点，围绕在y=0.1x+0.3的直线周围\n",
    "num_points = 1000\n",
    "vectors_set = []\n",
    "for i in range(num_points):\n",
    "    x1 = np.random.normal(0.0, 0.55)\n",
    "    y1 = x1 * 0.1 + 0.3 + np.random.normal(0.0, 0.03)\n",
    "    vectors_set.append([x1, y1])\n",
    "\n",
    "# 生成一些样本\n",
    "x_data = [v[0] for v in vectors_set]\n",
    "y_data = [v[1] for v in vectors_set]\n",
    "\n",
    "plt.scatter(x_data,y_data,c='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W = [ 0.96539688] b = [ 0.] loss = 0.297884\n",
      "W = [ 0.71998411] b = [ 0.28193575] loss = 0.112606\n",
      "W = [ 0.54009342] b = [ 0.28695393] loss = 0.0572231\n",
      "W = [ 0.41235447] b = [ 0.29063231] loss = 0.0292957\n",
      "W = [ 0.32164571] b = [ 0.2932443] loss = 0.0152131\n",
      "W = [ 0.25723246] b = [ 0.29509908] loss = 0.00811188\n",
      "W = [ 0.21149193] b = [ 0.29641619] loss = 0.00453103\n",
      "W = [ 0.17901111] b = [ 0.29735151] loss = 0.00272536\n",
      "W = [ 0.15594614] b = [ 0.29801565] loss = 0.00181483\n",
      "W = [ 0.13956745] b = [ 0.29848731] loss = 0.0013557\n",
      "W = [ 0.12793678] b = [ 0.29882219] loss = 0.00112418\n",
      "W = [ 0.11967772] b = [ 0.29906002] loss = 0.00100743\n",
      "W = [ 0.11381286] b = [ 0.29922891] loss = 0.000948558\n",
      "W = [ 0.10964818] b = [ 0.29934883] loss = 0.000918872\n",
      "W = [ 0.10669079] b = [ 0.29943398] loss = 0.000903903\n",
      "W = [ 0.10459071] b = [ 0.29949448] loss = 0.000896354\n",
      "W = [ 0.10309943] b = [ 0.29953739] loss = 0.000892548\n",
      "W = [ 0.10204045] b = [ 0.29956791] loss = 0.000890629\n",
      "W = [ 0.10128847] b = [ 0.29958954] loss = 0.000889661\n",
      "W = [ 0.10075447] b = [ 0.29960492] loss = 0.000889173\n",
      "W = [ 0.10037527] b = [ 0.29961586] loss = 0.000888927\n"
     ]
    }
   ],
   "source": [
    "# 生成1维的W矩阵，取值是[-1,1]之间的随机数\n",
    "W = tf.Variable(tf.random_uniform([1], -1.0, 1.0), name='W')\n",
    "# 生成1维的b矩阵，初始值是0\n",
    "b = tf.Variable(tf.zeros([1]), name='b')\n",
    "# 经过计算得出预估值y\n",
    "y = W * x_data + b\n",
    "\n",
    "# 以预估值y和实际值y_data之间的均方误差作为损失\n",
    "loss = tf.reduce_mean(tf.square(y - y_data), name='loss')\n",
    "# 采用梯度下降法来优化参数\n",
    "optimizer = tf.train.GradientDescentOptimizer(0.5)\n",
    "# 训练的过程就是最小化这个误差值\n",
    "train = optimizer.minimize(loss, name='train')\n",
    "\n",
    "sess = tf.Session()\n",
    "\n",
    "init = tf.global_variables_initializer()\n",
    "sess.run(init)\n",
    "\n",
    "# 初始化的W和b是多少\n",
    "print (\"W =\", sess.run(W), \"b =\", sess.run(b), \"loss =\", sess.run(loss))\n",
    "# 执行20次训练\n",
    "for step in range(20):\n",
    "    sess.run(train)\n",
    "    # 输出训练好的W和b\n",
    "    print (\"W =\", sess.run(W), \"b =\", sess.run(b), \"loss =\", sess.run(loss))\n",
    "writer = tf.train.SummaryWriter(\"./tmp\", sess.graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hkkVCd3e3950lPeOdeJZMokZVTblc2bOdFYmMRQ0NZnFLi5YS6hSJBSHEtKcS\nD8GGSy8tW+PPUdpEyTXC7rp9kiBImu2fR3a6ImBe7VxnkDArwTX6OednfMZe1ofCubbtZJkkEgYp\nxhgkCatbb73VO7YO/PUOcs6zuq2urZAppHKuW5f4dxKTh8SCEGLaU4mHYC7+boodCQZ/586dqYIg\nTQicEQSZ6YqAOY1iYKJrQOPbF1NMfywYWUo7XO6Knqc9QSQEnDJ3Ut5+Om6s48IrPhZXYFkRYUVB\nWv0HN81RSwn1h8SCEGJaEV9qyEqrsx6C1PVyz7577rkn9Zw3rFrldalv7OgwGy69tOCqTzq/Hcy7\nwDRQPlNvjPbfS2k1xoAwTXIX5TEBycsNj5hbb73VrG5rSy0rbXGF11kJY1tHeSpnDsx1Ke9XaY71\njcSCEGJakLTU0NvbmzqDv+666wrHJx1zI+Vr6H19fd6eDIsiw9jb25sZ/Lgidr6tidBC6BX4VIag\nWB077pcjEeHeM8mT8AH2lLWWThVM+XyJ8NqXcbxPRNhW1/H+FYpNqH8kFoQQ04Jqgw3vpLwOwXlg\nBjyGz2fwreGMu/+tW97OkOMu9X379hnAfADM70cGP34Pu4xwbYaImQ9mcey8IPosSBAJv8EfFIz1\ngujY5WBem3Gvvr6+QgVKd6ki6fgdCSIi7m1QbMLUQHUWhBBTmnw+z6FDh+jr7y+vNTAywrZHH2X1\nypV88Bvf4HsvvcR7gAeA64AzgE8CXwIeJqwJsBroAN4N/D5hG+dLgR3A/v372bhxI8PDw9ywfTtE\n5wBcBLwL+ExDA10bNmCM4cEHH6S1tZXLLrus0GK5r7+fHPCXzjM0ACPOdhPwKPCP0XZSbYJzgf8g\nrLGwPjrut4H/whRqMFh+id/i37mXLwNfJqyJ8FL03VPRGNLu9fjjj7N69WpyhHUZdkXvJOn4X4nd\nvz362d3dzStf+UpaW1sBvDUTxAyhVqqjlh/kWRBiWuFbdrA9COxs9kmP98B2QITkbot2nf9lYE7G\nZtfG+L0Y9pwNl15qNnZ0lHkjNnZ0lCw9pHWdbCTs92DH20y5+94+l/WYvJTgSfgwby0EN9pzzk8Y\nwyJKl0XupLxughvA2IVnGSaW+eBbzhBTDy1DCCGmJD6D3UyxAJHB37Z4YRAUDN/BmMH1rbt3kbxu\n7zunfd268uWQXM7kyG4K5esNYUVQ0vZJGrwioYHXlp1znbOdFljpCgNf7IHNELHNoZKEkeolTB8k\nFoQQdUGFSFXDAAAgAElEQVRWwaT4sWkG7xDJwYE3OoZtB9mBjYBpzOUKhq6SdslJ4/pkxv18XSft\n9WxtgmLmwHyvSDjCy8t6Q7RHhn3QuWbSGN4F5otg3p/xLG6GiBVB+/fvN8bM3EZh05nxEAu5itcr\nhBAzgnw+z4MPPsixY8fKvrNr+cuWLaOrq4ulS5dy+aZNnDp1KvF6J06cAJL7CLQDH45+jx9j19JX\nAHui3x+JHXPIOQbgnNe8ptBjIJcL/xf3pYRz0saVz7jfJ4FTzvZ8YDZhfMVjwIVAP2ezDQOxqIRu\nzsAQsJrvcQXwF4QxCbsJ+0K8HbgBCDLG8DeEMQifT3iWc5yxfocwXuITDQ10dXayceNGAJqbm3lg\n3z7y+Tx9fX3k83ke2LeP5uZmhChQK9VRyw/yLAgx7sS9A1mVE40pXU44GM323Zl80n1ImfXeeuut\nZvXKld5jPkWxHsH5JFc/jGcZbOzoKItFcLs4VlLmeXbK/VY4P+MdI8OxXOD1JAQ0ZHor3Pv/OcX6\nCEmdL3uw2RTFZxnCXzvB/k2PHDmiQkrTGC1DCCHGTJIosGvXSUV+rNG/K8EQDQwMJN5zcUuL1+At\nbmnx1jF4kmJaoy/o0d32FRtqCgIzl/L1e3vuqrY2MzAwEPZA8IxrLmGDqLsJW0qXvCtKqy3Ojo57\nBswfcKlXJDxAaR+GtOUCKxwWOPdqj41hBaXBofdFz2afpQN/hctFjY3egE4tOUwvJBaEEGPGmx2Q\ny6Ubsaj+AJEh8kXnr25r897P7XoYN3j2956YUbQzejcb4UnPNSoxvnafr1bDwnnzygojrYiEworo\nvL5o/72x67k1Cu5lm1ckFIQUxT4M8eJObm8Id+xuLwZDGNthx5jkmXCfL7F8dexvp2DG6YfEghBi\nTCQtCdyYYYT6+voK52aJijhukGE/mJ1g9lMaGPgk5d6KeDaCL22yKTKQWUGIBn/TJOtt2E0xIDEu\nNmygof+Z/zhRJNjyzk1g1jvX8GUllCyRUN5Uyh1T2vtftmSJOT3jb5lUgKnSYFUtX9Q/EgtCiDGR\nlB1wsEIR0HTGGZmiwmINi+3nkOZZuJDyGgEXOUbPGuwd+Hs/7E4Yd39kbPszni+taZIVKm79hEu4\n1ysSDMXMjv3RTrsdf295MLfjaRFN6TLHIYrLIx2U1ks4SCj0FoA5s6nJK7IqflbnbxenkngWUT9I\nLAghxkRasGEOUvPtK/Us+AzL6bNnm/mRsbctl23Mwvq1awvFjOJ1GHKEMRLxMsvWoFpDNy8yyvdG\nxvIMMLNi54xmtm2bPN2FFTMDXpGwC/8ySTzw0hdrAGHwZryplPWa2HNtgaVhMBsoX1J5/WtfW3jG\ndsqDIu37TBJWad6CrE6g1fz7k2di/JFYEEKMGfs/fl+HxbTZYzxmIR4UaGMW0iomusb+ruj3P/zD\nP0wVIL521NZVb4+JG87TKC45HARzJekiZ6HneU4rueaLXpHgLqUkFUWKV3tcEbuPHXs8dsK33ZTL\nJQYw2mqMOwhjJOJCxX1H7rJHUxCYjR0dif9esrJZKjH88kxMLBILQogxk1WEx22m5M4ErdG4m+Rs\niEoKMFljbysb2k6SoymcZD0IrpG2bvh41kZSGuR8ypcCioa6XCBg/9fpjONzGeNMqva4rLXVnPPK\nV5qFQWB6CAss/ZrnmWzsw+KWlsx3Yj/rwCyl2KgqSbzNhVSxkFXYKm35wlIrz4SoDIkFIUTNiHdY\ndMlKr7wvMvw3UqyzMDg4aHbu3JluWDyGzcY0+GosZC0fWCPvuvdt9kI8a+Nu/L0T3N+3gnlzikiI\ne1RsyWp7z6znfiZ2f1+th/SASszNN9+cei8bWNlIdrbIfrI9BGP1LNTCMyGqQ2JBCDEhJM0EfUsV\nvmZMWTNsa9iazjjDDA8Pm40dHYVZv2/tP2mt3/VU2NTDwQrGkTTbThIJJrp/fFxzCT0YByt8brv9\n99GxC6KUVdfwZwmPPXv2ZAqAuCBLEzFpwanWkCctXVXiGaiFZ0JUh8SCEOOEAq+KVDITdL0ScWFh\nqxr6ZuDxa80ndIEfOXKkMMvPWvtPu541yudlGEkozaxIEgm+2Ax3yWKB87tvmcMXo7DauYGvJ0aW\nZ8F95/F72XfYRSi6KhFvWcGpXZ2d5uTJk6OOOZBnYeKRWBCixijwqpze3t6CoR2kWH8gaQYaNwRp\nM/B4ESJrLG+++WaTo7xscdyw2E8HpZ4G1wV/H6EIiQsCg68wk18k7ALTS3nlxLmEJZg7YvtzsZ/2\n4wsyHHBudJBQfCwMghLDP5cw8DBpJu+LO7mAUCBYT4v9G9jOmnHx1uG5blZsQdrSVRpj8UyI6pFY\nEKLGzOTAqyRvil1HT6qL4B5vXcwHKdYz6KNYbdAGL54fu9YGMBs9htS2ZU7yCNjvs8REPJOgg2L6\nY9pyg12icM9dTjEIsofk4lAXgfkIYQrkGRS9DDa2YwHFqpBpvRsAc/HrX28uWbMmVcQODQ0VemoU\njqG0RkP72rVeYZHzXHc8PQDqbDmxSCwIUUNmknvUFQZp3hT7TprwLwWc2dRUct3HHnsscVadA3P1\n1VcXxIQ70/cZ3EaKfRjS3OZuUaJnCL0T8yJjHuDPJCguHySLhEaKfSZ2EXopdkfbtmaDfQ47Put5\n2el5/jNj27NzuYIHwZv6mMuZxvnzS85pX7vW9Pb2mv7+/jJh19XZaZpzOW9KqRVXvb29heNdr4DP\nQzARsQWj9UyI6pBYEKKGzITAK58wWNzSkuhNse8kzWDfdNNNpru7u7B2Hu/h4EvPc2fkvnV69/rt\n+Os4zKXyJlPxayeJBPfvvZBSD0rco2LFDvjLU8+l2FCqJ7reL4P59ej7BUFQUerjbucaTblcyTlW\nQNxyyy2p1yj0lajCKM8k8TzdkVgQooZM1f85VhOMGV9mySoFfNNNNxWMUlpwoGuYs655NmGBo52U\nrvUnXf9aymMC5jjG3H4WEXpAfALFXjt2ixKREB+nfaa46FkU7Z9FuLxgBUT8OBt06VtimA3m09Hv\nWamPbj8LGyyaJMKSYjcWZLQNz/r3otiCqY3EghA1Zir9z7HaYEyfGMpKy3M/WYWVrNH6pOeaQx6D\nn6M0HiAr7sB+PvShDxX6HlDxNfwiwS5RxAszdVRwzf9B2BAqq3bBmymv5zAXTEv0e5ZXwAZkZmVF\n7CY5K8TGKlSLYgumBxILQtSYqfQ/x2qDMX3LLFkGyAoBGwhYSfqjz8jaxkvxmfdcwqj98z3XbwJz\nCeUZCI3z55uFQWCuJBQmF1AsxRwXPbHNwseNO4gvXbilp9OE1PzYeUnH5RKe3d63fd26Qgnr+FLL\nBRTjILoz7tMX+7vdB6Y5lzPt69aN+d+aYgumNhILQowT9f4/x9EsmSSdU0kdhLs8RjXJ5b0jEgFN\nFDshZnkOBiiPDbCeh6SlBXtcC+WxCbFbOZ/wuAbCZYye6DkWEmYouB0dfR6DQYrtu20Rp0qeL3FJ\nZvFiA8kls31iJs0DEfcI1avQFROLxIIQM5TRBGMODg6aVW1tpjFqPmSFwcIgMM0LF1YkBFzX/90p\nRssnLtKWOv6Q8mWKrEyIL1Lq9ViRIhJ2OdedH7vusMdQvw7MOYSxEc2E9Rji43OrSMZbVlvPSNaz\nW7Fgv28nFCFuUKOtgWCftYnyZRMr7Oy7sQGnQhhTR2IBuAb4NvBT4DCwOuXYS4BHgeeB/waeAn4n\n4/oSC0I4VONZ8MU2WEMeryHwy+eck3pdNz7BZiO4Rms9pXUVALMgSv9Lm3mfi39dP3AM6aDn2usL\nv/tFgltfwT6zzWCIG3B73XNi42jAX0XSNdLDlIuJLjBfIdvrYL/PrNSIX4S5HSPrNb5GTC51IRaA\n9wAvAL8JvAa4GxgGzkw4fkV0znLgXGAL8GPgAyn3kFgQIkalwZhdnZ2mMZczOyKDaIsRxT0Jd0cG\nsSH6+SnC2gK7yI5PgOQWyDnH2MaXOqwgsL0ZStIEo/N24nfR/2aKSLA1EdxZuV2++GqGUY73iMiq\nIhnv87CU0hbXF3ievRHMm6LjV7e1mUUNDYXljXhMyb3Rfrdvw4UXXGB6e3tNe1QwqyBQtOwgPNSL\nWDgMfMbZDoDvAh+u4hp/Ddyb8r3EghAxKgnGdIskuUY9HgewiLBHgXXXz4qdcxaYk5EB82U2zKI8\niK8xMpxJgYQN0Xe+AEO36qA7q7+PsGWzXyQMV9Tz4FXRNRd4DHjOIwp8Rtw4hvte53w7y4/HX8Qr\nSG6kGEQ5MDBQ8nfswZ9u2U5p4KX1HtV7fI2YfCZdLACzgReBd8T2fwH42wqvcTHwH8D7Uo6RWBDT\nnqx6CUnfpxmL1W1tJe7zrBoI1rDFxYTrWYhnNiRd806PkbSlkpdT6sVIqzo4D8xrsJ4Ln0h4yLwt\nw6D3RUY8qWiT+3v8GgcreGdASTonFKst5vN50752rZkXZXDYIMq4Fyifzxe8DEl1G+ZSFGrToUiY\nmBjqQSy8HHgJeGNs/yeBr2ec+x3C5YsXgT/KOFZiQUxbsuoljLa5VVpdhYMUG0K5RvXKDMPY4/k+\nqVbDmgQhYGfdOzLuZUXIhxNEwizuKixxkHGtfHTfBdF93VbWrkcl6RpJyyhzKI37cAWCS6UpucPD\nw4VeHEnPstM+kzwJokKmulh4FfBa4ErCYMf3pBzbBpj169ebt7/97SWf+++/f1xerhATRVa9hNE2\nt/JlTDxGel2BmxMMfzwbwv3+Lz1GNitYDzC/m3Gv+Qki4eP8bpmLvgnMOsozEpoJGzpdl/HcNrhx\nNuXZBo1g3rxmjdnY0VFyfi4SC7ak8y7CSonta9cm/k0qWTLIynQZbTVGMTO4//77y+zk+vXrJ10s\njHkZIjr+j4CnUr6XZ0FMS7KyGvr7+9NnzClGx3ftLspn+9a9XckM3ZZ/jq+r5yjt35C1zp/mWfir\nBJFwN+8sPIOv78RrKS/g5AZZ+rwc1qVvx+trAJWj2IApn8+b7u7uxPdgP0uXLDEDAwOJfxcrGHxL\nS1n/JkZbjVHMXCbds2BCQ+4LcPwOsKOKa/wpcDLle4kFMS3JmkXu3Lkz9fusdeuuzk7TFHU2PJgh\nBP4++m42YZzALkpn6DnCaoPW8Np1devCjwf1pd3Lxiw0OSLj2+S8IuEzrC5cqxKPha3PMI/KiybN\nJww6tO+1m3B5JakBk/t3s+LlLsoDPzd2dKQuJ7nCxF2W8GW61Koao5h51ItYeDdhvQQ3dXIIOCv6\n/hM4mQ7Ah4BfAVqjz5XAD4GdKfeQWBDTkvH0LBhjzJEjRyoujtRL8szcGsF5YN7hEQPWYNpWzrvw\nl4i2QY322o+COYv5XpEAv1wY6+Lo3CyPxQ6KwuYj0XdZ/S9mRdd3MzCSghDjfzc3uDPJ45G2nNRE\nKG7cpaWhoSGzsaOj7O/WdtFFid4KIdKoC7FgigLgacKiTF8HVjnffR542Nm+Fvgm8F/AKeBfgKsz\nri+xIKY0aZkOWfUSxtLcas+ePQVjkxW8eCb+JYo2MK/E755/knC2nyQ04ue8ITo2XII42ysShmkq\na1t9ktI6DpWkSBIZ8CMZ59iAwV2Ul5JOCyTt6uw0C3I5A9neiyzR104xfqJ93bqCqDgUvasFzrhU\nS0FUS92IhfH+SCyIqUpWJsPg4GBmcZ3h4WHv90eOHEkUIHZ2Gvco2NlvvCxxc4axm+0REnYZwb3H\ncuf3eYTtqF9O6Oa3yxqf4AKvSPgZs0tm/TnKAxZPx5+Z4FZTdL0Mdr+vHHM8RdMKnDevWePNaIjj\nZi4kVYWsdDlpAZV1utyNqjSK6pFYEKLOScpk2NjRUSYi2tetKzNSPrHhi8yPi4euzs6CcXd7LPj6\nILgFmJKqB2YZMFdAWLe+e49w6aHDKxJ817aehXg5arudVMgpycuwk/KqlfHiT7bXRCXG2PUUvWHl\nykKVx12UpqRW6lnYFRtbWr2ISpeghLBILAhRx2TFIzTmcpnpkBsuvdTbM2EOpbP8piBI7E44THHN\n386sdxHO/BdE+921d190v11ySDJgZc/mjO9q3usVCVdSXgraV/1wUWz7ZdHPjxN6NuKVGH1eBvsM\np0Xv78PO8UmlrOMZCn19febIkSNl4m1jR4dpaWwsK0C1grDss7uc1Bxr4hX3blx4wQWp/2bczpIq\nyiQqRWJBiDomK9NhR5JBiIzU4OBgwRUfjyPIUZy9uuceAnMe5bPTYYoehvgsPR6k6KseaLMfkgyY\n79n+hJ1ekdDqGO9GwjoFHbF9vnsfjK67kNBTYTMQ4s/l8zJsp7yapBVXp+HvsNnX1+f17MylWFvB\nirwzm5q84z6zqSl9OYlS74Yt/VwWo0J5Z0l5FkSlSCwIUcdkeRYOxfbHZ4w2ODGx5gFFl7c9d49j\niJLOu5Bim2YoVnMcoNjFMc1d7mY2+AXE/+sVCa4Y8JWI7k+49534gyuXxvY1RIbfHaMVP3PwB2/a\nGIv9nme1yznxZaS4JyIejJkk/izta9eaBblc6Tgdr5Kv2qM6S4qxILEgRJ2TlMmQq8C4WLEQ90z4\nehxY13139LOD8kDGRjCvo7iGv4DymIBVpK+Zu59ZlHZXfCXf8ooE99msUfWlMyalOHZ4DH0jYdDl\nLjC3UxpY6XoNcrH7+t43YN7lMdyZbcCj7U9mvLPu7u6SfxOVln7O5/PqLClqgsSCEHVOkmHY2NGR\nmQ6ZZKxWJBjPxRSLFt1N0UvgGtA/wy80noyus7ACw2o/S7Gz+3KBEH6K12+iVIj4iitVui8+nqSl\ni2Yw1zrHVSKAXGOctYxkYzXenzHGuFiwVNMtUp0lxViQWBCijnEj5uP/s690drmxo6NQgfEZsl3e\nf0+4zBAPipwTfV7nMazx9XBfaqJtN22vd06KSEgSMvF6B740znghp6wiTGdnvI/ZhB0r045paWry\nGuNKlpHuIwxU9aV5FmJLZODFJCOxIEQdUk2XyKwZo09UpBnPgOSZdjygMW783NiH2R6xQeEafpFg\nr78Df/BlntJ6B/HMB9/SQXwJYZDSMsxWFKV5De6OrhnPRGgEc/rs2ebkyZOJf8u4WLPnuWO8ZM0a\nE1Au0OZG70tiQUw24yEWcgghxsS2LVs4fOAAPcAzQA9w+MABtm7eXHbskiVLuOyyy1iyZIn3Ws3N\nzTywbx/5fJ6+vj76+/sBeCR23KHopyFsA3s7cAVwTvTzL6LvANbHzm2Pfh53rvMJIA/0Rt//PLq6\nKVyliCHgTgKCaHsXsBS4nLBEq73+14F3ENaG3wa8Hngi+v4i4AzCd/U0sBuYD7wZWAH8NnAxsAzo\nAnYADcDPomsnvQ+Aj0Xv5PWXXMI24Nzo/stWreLfn32WV7/61WXP5PJTY0rOe4Fw9rIDaMzl+PnP\nf44BLomddwnhOz9+/Hjq9YWYktRKddTygzwLYoqQGRQ3illmvFT06rY2bwVDtwJg2kw7LdPBzprt\n+V0pngT7yyBhG+ik5Y14vEMOzB8QxjA0Urq0Yj0HbkGlfyDMdPBlM3RQjOGILwF04CyDtLQYY6pb\n+3f/lnnPuOKlpROPk2dBTDJahhCizsgMiquikE7ScsbnP/95bwXDu5zt3QmCICkeIb4EsCNFJNhr\nHaG0eFOSCJlPuYv+LMJ+D+75HbFj7HZWAOEA5YWcvBUdqzTalQQ42t9XtbWNun+HEOONxIIQdUat\nPAuDg4NmVVtbSZXHuzxG9zzCoEZr9N1USDc3vzE6dznlMQHtYO4oEQt+kRA3lucRzuCz+iLMxx9D\n0R4ddw+kFp86LeP6rtGG5PoVe/bsqSqjoJLUyXgxpbiwU4qjqAckFoSoM4aGhszilpbyZQJPWqTP\ncHm9CRR7OsQNqusVcHs8QDHAzoqAOWCuoOh5KO9h4BcJSZUb3aWDNKOa9t0hsj0HWdfwLQe4x/mK\nOlVqyH11MuwSh+/vqhRHUY9ILAhRZ3R1dpqmXK7MLb64pcUMDw9nZkrY6PuSvg9gLskwmGkzcyht\nPhVP80sSCe7141URbQVIN7Yhnga5CMzq2HHx2b77STrmFWefXYhZSDTahJ4U3zKLTcfM6sPhw5eN\n4goPeQ/EVEBiQYg6Iu62tsFuNs0vqXxwpRUDs4xu0nm/8zu/UxjjkSNHzIL58yMvRLJISDPqvqqI\nvm6WXZTXVoiPbQ7FXhZJx1zwmteYhZTHJeRi93oy+v1cz5jHuizkegzkPRBTjfEQC7MQQpSRz+c5\nceIEra2tiWmOJ06cAGAEOAYsIfyv85vR9729vfT199NDmM5I9NOMjLCtv583f/nLQHJqI4Qpglc4\n226KYNJ5t956K/mnnuKzd9zB2y+7jB//5McJTxnQE9tjrz8PCIBZhGmZ6wnTIK+JnrEd2ByN7+fA\nnGj7bMLUR/e4Q8C1QA74BTAUHbPdc8wla9bwT4cPF97ZMcIUz/9DmLrYHR2/BApjvy/atxN4BXBV\nyrs5fvx44t/TZcmSJSXHVXKOENOaWqmOWn6QZ0FMEpUWWBoaGiqr4f8y/DPhJO/A1VdfnelZiLvY\nm8CckTF73k3ovUjyJNhCTj4Xvk1/9FWOPALm/IRnjMcInBnbXkEYmGmv6fNMtK9bZ3p7e1Pf2Y2e\nsbqxDFnxFPIOiJmAliGEGGfSlg3Sjkvq35BLMVyrV670lg12gxjjbZYXUOz+6IsZyKqTYMs7N1Pa\nKtp+FhOmON5LUegMeQz72YRxDK8+91zzuc99zgRg5oG5kmJZ5IWEAZcfiQbgaxyVd+7V19dX0dKM\n/XSA+Vh03+WUxiwkVWFUzIGYCUgsCDGOVJoGGT8uaza7wGPU7Xr8m9esKUuPdBs9NXoEQ9LMPE0k\n2M9B5xo+ETAXzEZKPQs2mDEuhOy44l4Fu22FyL0Vvif7fn0ZCbbQ0kMPPWRWtbWl3ndjR0chuNMV\nFndHImJjR8ck/isTYvyRWBBiHKmkwNLQ0FDBWNnjklot2/PibaHd4Lze3t6SZY94caWPec7voFiA\nqD9FJPiu5zZq8omAJorLE02EQifNwK+hPCujCcwbKF0Wsee5vSLcNNP2tWsLQYS+jIT2detKPALt\n69aZZqcmRQ9hL4j2deuMMUVBl9i3ImE5IinFVYiphMSCEONIJZ6Frs5O05jLVeVZgNAVX17noGi0\nuru7SwSHb9ZvZ8fNJC83zOGFwkzcl35ojb/N2Ega84WEyxE2c2E0WRmzwZw2a1bJGHyFpha3tJSK\nqWipICkLoZK/0549e1LHHW8jXU0zMCHqHYkFIcYZnwvcl+oYrzOQ1K8gB4UOhXc53zUFQVnRJtcA\n+mb96TEJAyWGbgVhIGRTzDDb2gRWNCQa02jbXbZIEkJJ15gHZu2b3mTObGoqEwcPPfSQ6evrM6tX\nrjQLcjmz231OT4yIO+OvxANkxULSuONiodJYFSGmAhILQowjQ0NDZmNHR9la+JvXrDG9vb0ls39f\nzICvomILoTcg3uZ4Y0dH2ay1fe1a05zLeTMRTKJI+CuznKK73+e92O/sjy+NpFVJtNs5MIti7Z6b\nCcs3p13Dei/OCAKzmzB2wWZqbLj00rJsElu50vUQ+Gb89rw0z8Lg4KA3eNQKONdbMR7NwISYTCQW\nhBhH3NnlITDXUu4yz0XG31Bcfvg1MH/tEQ/tlDc3uvnmm01/f3+Je901iK6gsDNnv0j4qGmJjnt1\n7Pj4TPs6Qk+BWyZ5FslNpl5H6FG4kTCjYTaY02fPLhNGnyZcGmmKXcN6QOz9z/MY4RyUxRzEz+vr\n60uc8S9uacls5LSxo6Ps7zcXygIca9kMTIh6QGJBiHHCnV3atsnteIL3gsDMJew/cFHMEHWBuTX6\nPam5kQ2OdNfFN3Z0lBjEjxS+94mE3yrzUsRFjCGMeXhd7JggJhJ8qZPx5lR230Io8Q7YQMi4wPF5\nCFxPhcG/tDFIMfjSeiT6+/tTZ/xlnolYjIEvUNIXhyDPgphuSCwIMU7Y2WXceCYZkABP/wH8rnnX\nEDbGZtPxYMkkT8KH2VgQCa/zGPS5hKWU4zEU7r3m4s9uyDvja6cYK3GQMJtgQXQ933v4ded9LKC0\np4Tbq6LPOff9FAWVL5BzDuHsv5IZfyWlmCs5Ji1WRYiphsSCEOPA0NCQWb92bUlzJrcokc9QpQmJ\n2YQz7zspFx8rKC5NuIbzawQJnoTXFs69CMxO/I2SXMPsjs96SfqjfVntpSEMxIwb8ByYAZ/BBrMq\n+j3+rO72LsJ00bKGW/hrOGzs6JjQGX+lXgghpgISC0KMA/F0yCEw6zMEgWtwrUHuifYvpzj7962Z\nrwTTi/VCzPGKhD/ilQYwv0l5Uaa0cb0NTGeC8c6B+WoFz9WRYMBX+ww2pc2jbDOtforeipYoG8Iu\nf9jrZqVvuo24JmrGr6ZRYjogsSBEjXGL91gBYNMWfSWV3XgB3wzcLg/MJquNdGOCJ2FB4Vqne4z9\nW51xJnkGSLi3LRPtay/dGI05S4wcco53lya6SPambOzoMF/5ylcKHgabmZFVzKqvr08zfiFGgcSC\nEDXGrosfpDS4zpZUXhwzfCsIg/1Oo7gcEHetNzmGN250b+WXvCJhE7O8xv11HuObZcyzZuw7Pdec\nC+as5uaC2MkSI4tbWkyTk05piy3FvQc2e6HtoovKlknaM8bpzu414xeiciQWhKgx8UJLbrEimxqZ\nVL/Aigefy35RzOg+weu9IuElsitA+gIVfSmPG6Pz9lRo8F1PSFdnpzly5Ih597vfnTqWK6OfAwMD\nZTP+9Rn1DxbERQShsCrrfKnAQiHGhMSCEOOAXRe/i7DXgTV4WW7yrFk+YD7KGq9IcM/Lus+O2P67\nKA9m7ABzgtJlkUo6N65qazMHDhwoMfy++gu+GgjGlM74s7IX4s9hx9MSq/CoZQYhxobEghAOtWr6\nE18Xt8YyqZJi3Oj6jWOnVyQ0E3oGbBbAfWSXVE6q2XBaTBjYuAFb0tln8O3yw/bt28u6PNpZ/4WU\nV7b5XTgAAB3lSURBVKPsIgxktEGLvneelb2Q9By9vb1aZhCihkgsCGHGr+mPbUB06623mvZ16xJn\n2c1gzsIfl/BXbE30JFije1f0+3Jn/EnVFHMe43sn5Z6FOZQui/gKLrn3vummm0xfX5+38NFwJBhy\nhP0dfDEOSe/bl71ggzpV9EiIiUFiQQgzPk1/fAJk9apV5p577ikIB9foniSMEbCdHd/NRxJFwiFK\nYx7cZYw2wjTKgUiAuPdpolj8yTW+vjoLbpZGUsGlXYTNpeKpmDnCGgi+Wf+rzz03MWjR976Hh4e9\nPR82Ut6nwY5Zyw5C1BaJBTHjGa9CPT4B0kgY9T88PFxoIhV3pb+DW7wiYZjswMV5hLULrIjYTbnH\nYBaYs2P7spZG0mIskoRGUoXGrJLLvvdtYxfudZ5tGH/RprtThIcQYnSMh1jIIcQU4sSJEwCsj+1v\nj34eP3686mvm83n6+vu5bWSEK4BzgCuAvwCeHRrine94B+vXh3f8TnTOddxGgOGr/E7JtRYQcB8B\nPwaOAHOBa4Ce6Nwe4HqgC7gbGACWAhcDfwDMjo1tBPh+9PsqYGv0e9LzAzwS++5Q9PMDwM+A26Pn\nc5/zCWC3O8aGBro6OxkZGUm9n+99n3/++QA0AEuifc3A+6LfrwTywEPA1cBnRkbo6+/n2LFjZdcS\nQtQHEgui7snn8zz44IPs37+f7373u0CyQWxtba36+lkC5NCjjxIEAV2dnbyPvyXA8BdcFzs6gEgk\nvBc4F9gG/JzQUG5z9q0hNMj2+juAbwMvAacBK5yrGuAsQmFxErCmOen5F86fnyhO1kXHJD3nDneM\nGzbQs3dvwfBX876XLl1KV2cn2xsaSsaxPZcjB3yUoohw7z8aoSeEmCBq5aKo5QctQwiT3LqZyJ1+\nF7XJzc9a2gDM+ef/wLvcsLilxSzytFpejdM9kWLqYN5z/TzFzIukug3tzvE5yttCNwVBZhfIrOyO\nz3/+896MhNGUXPZVXmzPqMOgQEchaoNiFsSMwhopnwF1jSOMPUiufe1ab6rhHL7lFQlJmQSu8WuM\nDLXBX2J5EWEPij1g3uc8S9L1/p7kttBzCdf/bc8FXxfIpCJIbnCk7z1W0+o5LjbiKZHq7ijE+COx\nIGYMdrafVbq4u7t71DNS17gNDw+bxS0tjkH8sVckuAYyqwjReRQ7TA5TXha6JbZtDXbS9c6nNDhx\nN2GQZOB5PxdS3sTKXr/0OcNxPUl2VklSLYRqUlnV60GI8aduxAJhzNa3gZ8Ch4HVKce+E9gP/Cfw\nQ+CfgbdlXF9iYYZjDfHtGQbUVhKshiTjdvLkSa9AAOM1pPv27UsVMguCoHQGncuZM+bPLxjupPTH\ntOWQSgsePUm59+Hiiy4yAwMDxhhj7rnnHkMkOHzXq0aAjSaVVUWYhBg/6kIsAO8BXgB+E3gNYezV\nMHBmwvG3ADcCK4HzgT8jDMq+KOUeEgszHOtZeE2GkRyNsfEZtySR4OsJYe+5Z88ek8O/vJADc+EF\nF3hn9lmGfwHlywRZDZ6SSil/MfquMZczXZ2dZUJprCJsvFJZhRCjp17EwmHgM852AHwX+HAV1/gW\n8Mcp30ssTGMqLdNsGxOltYqu1oUdN25JIsEXJPhkzJBef/31hfHF3fqAufnmm00+nzer2trMwiAw\nd1FaayDJUAex673uggsKLZ7T4iPiVRO7PMe1r1tnFjU0ZC7vVGrks5ZiRuP5EUKMjUkXC4Rp4C8C\n74jt/wLwtxVeIwD+DfhQyjESC9OQass09/b2GiIj7TZIgtEX9LHGLUkkWGPt65BoRcCRI0dKsjSa\nCWMr7o1+2vLG3d3d5rHHHisYZRvk6BrqQYrVHd3lhjesWlX2njZ2dJQHBxJWR/S9Hxsv4RpvVyB4\ngy6rfJ/yLAhRf9SDWHg5YTr4G2P7Pwl8vcJrfBh4PmnZwkgsTFuqXdt2DZGthpiUftjf31+RtyJJ\nJPg6MtqUw5KZ+dq1Jc+xjvJAwrmR4LBeBShvFrUh4bw5hAGIvve0saOjTGzZ4MT7CJca7NJHJRUe\nhz0iYzTBhspwEKK+mPJiAdgC/BdwacZxbYBZv369efvb317yuf/++8fh1YrxZrQzUGuIbowZOt+M\nOc3g+QTC6fygZFljLuXehK7YfW699daS5/AZ3LlgNnZ0FJ7Zihx3/BsJ0xjjAY6LGhsz31M+nze9\nvb3lPRii504y3usT6hxYT8f+/ftH9bdVhoMQk8f9999fZifXr18/6WJh1MsQwP8AfgxsquA+8ixM\nM0a7th03RElG9CP4vRU+kfDa175YZtxyhMsaXgPt/G57RMSf45DHUNpn7oiEQNxLMpr+DvH35Msq\nSDPe4+kFUIaDEPXBpHsWTGjIfQGO3wF2pJyzGfgJ8CsV3kNiYZox1rXtm266yQRgFuLPPOgru2a5\nSFi9+tnCfQYHB013d7e5+eabMw30jY5BzXoOd3Zuj72b0Ptg0yUr8ZLUIgagWiEhhJge1ItYeDfw\n35SmTg4BZ0XffwK41zl+C2GJ/A8Ci53PwpR7SCxMQ0Yzq/UFRbqZCjbo0MYx+ETCOb/0QMn5zQsX\nlmy3NDVlzvTb160rGFTfcyRlZ7jH/gNhYaUsQWAzFsYzBkBeACGmL3UhFkxozD8EPE1YlOnrwCrn\nu88DDzvbXyNsnhf/fC7l+hIL05DRzGp9QZHNYC4iXGtvLBjgcpFw7bXPF863aYu+YkhNuZyZ29Dg\nTT+8iHL3v+85krIzknoktF10UaIgmIzZf6XprEKI+qduxMJ4fyQWpjeVzmorafDkEwnzgg+VLRl0\nURo34J5wp8djYbcvtJ6L2FjttRObQ8WOjz9zJYJgImb/1aazCiHqn/EQC7MQYpzJ5/OcOHGC1tZW\nlixZUvhkkd462pQdP49f5wX+mre8rZOevXs5fPgwAL8E9BG2YN7lud6XgUbC4JtfAL8N/AphIM41\nwOKWlrLx2rFdB5xjn5MwVQjCdsvuOfFnbm5u5oF9+zh27BgHDx4kCALa29tpbm5OPGc82LZlC4cP\nHKCH8L08Amw/cICtmzfzwL5943pvIcQUolaqo5Yf5FmYFqTNWitxe/s8Cz5PwgE6vLN6d/YP5bUO\nDMXMhGorGrpjG6I8fdKNcRjN+5kIVFBJiOnJeHgWchMlSsTMw521PgP0AIcPHGD5kiUsW7aMrq4u\nli5dyuWbNnHq1Kmy85cuXUpXZyfbGxoIMAQxb8I/sBJDwFt5GLAeh3BW756/Jxf+M/8u0AVsj8by\nHaA7Oudl0U+/F6N4Td/Y3kqYIuQ+5zf/+Z/Zunlz4rvJ5/Ns2rjR+37Szqsl6Z6b8mcWQsxgaqU6\navlBnoUpT9asdTdOMaJczqxqa/POZH2ehDeuflfFM2IbG2ADG+P9GexnNL0ShoeHEwsdJZ0X9yZM\n5qxengUhpifyLIgpQ9as9SfA94H7gR++9BL/8vjjBS/D8PApggCCoPTcL33pH8nnj3H4yF8XZvXW\nQ9ADXN/QQFdnpzc24LGBAZa2tfFBiPwQsKqtjYGBAbo6O/lEQwMrKPU6JF3Tvfbv/+Efpj5nfHZu\nvS07ou3JnNW73pFKn1kIMUOpleqo5Qd5FqY8lWQy5CgtefxX5LyehCeeqF1xoaxCRWXdJjOuWc3s\n3NfrYrJn9SrSJMT0Q9kQYspQmLUeOIAZGaEdOARcC6wAbgXeAvwF8BvMZi4/L7vG9743zJW/tYUV\nK/oL+7o6w0wHN5vg+PHjhUyLLHwZBvFrzZo1i1/84heFa+bzeQ4fPuy9R9JzXt/QQNeGDSXHu96W\ncyjGTxhIPW88Ge17FELMMGqlOmr5QZ6FaYFv1rqCsPlSHxg43etJgJzp6+uruktlFtUWHqo0W6HS\n2XncC1Grro9CCOGiokyiJkx0tb58Pm927txpiIz+AI1ekfCS44bv7++vmZveZ/RXr1xpfv/3fz+1\n02IlYsV9l5UUUfKWik4J8BRCiGqRWBBjYqLz+l1DGs6qz/CKhLsoL3k82i6VPlyj/yTFfhL2s7il\nxZw8ebJs7Gli5ciRIxV7E6qt3CiEEGNBYkGMiVq79ZMoFyV+kdBDsQFT3HCONq0vbpzj1+ki7FTp\nvoPGSDC4ZImV1W1tqe8yS5ipkZMQYryQWBCjZiJz6q0ouZsmr0jw3b+7u7tsDNW47IeGhkx7VPPA\nNc69vb0Fo5+VgeBrL52WzZH0XX9/f6aYEEKI8UJiQYyaWrr10wiN7DxzPl8vEQiN/IcBzKEq7h93\n2SelNQ4NDZnFLS1l3SQXNTQUBEQPNqgy+R3s3Lmz5P5JLbVXtbWlXidLTMibIIQYT1SUSYya888/\nHwgbBbkcin62trZWdJ18Ps+DDz7IsWPHyr770Y/gssvOBn7CCdYAsJH9GAK+ySsA+Icq7m/T+vL5\nPKvb2miKigfFSyO/81d/lWeHhrgduIIwLfEK4DMjIxx69FHa165le0MD34qum/QO3vSmN5Xs79m7\nlzUbNrANOBfYBqzZsIE77ror9TofiH6qjLIQYtpQK9VRyw/yLIwLSTPlSlzjaWvwp04Zs2xZySTa\nXMu7vbPqxlyu6vtX1qo6eabf29tbUnSpMTq3sLRBecyCiy++wLtEQhg8WS8Fl4QQMxMtQ4gxMZZI\nfF9wZFPuLHP6ad8vEQl/93fJomRjR8eo7p+1hGI/WcY5n8+be+65x5zZ1FRyXktTU1k2xGjeZY4w\n28JQDKQcjTATQoixILEgakK1kfjxmf33eZl5GaUiwQ05yBIlY72/z7OwwmOcGwlbRccZGhoyb1i1\nalSiKY59lnhdCBVcEkJMFhILYlKwM/sjvMIs5Aem1GZ3JAZH1jI9MG0Jpauz0zTlct76CT7jPF4p\npCq4JISoByQWxKTw8MMnDPysRCQ8wtoJXYNP81b4vmtfu9YrFMYzhVQFl4QQ9YAaSYkJ5cQJCJMU\nzivs+yhv5EqOTHjTo6yGR5U2Q8pqnX38+PFRP4+aMgkhpisSC6KMwUF4zWtK933taz9i1/96Nzv7\nj7Az2te1YQM9e/emXiufz3PixImaGU5f18hKvrO4KaRXOPurTSEd7RiFEGIqIrEgCnzrW/C615Xu\n++Y34cILARbylrdUPmseHh5m25Yt9PX720tPFtW0lBZCCBGiokyCb3wDgqBUKDz1VLiYHwqFIkuW\nLOGyyy7LNKrbtmzh8IED3iJKaYWdJoKkYktZXhIhhJipyLMwg3nsMVizpnTf8eMQeepHTT6fp6+/\nnx6Krv4rgP8aGeGa/n76li0rHDsZ3gbFFgghRHXIszADefTR0JNghcLs2fD006EnYaxCAZKDCL8M\nnAFeb8NkUKmXRAghZjoSCzOIhx8ORcK6deH2GWfAd78LP/85vOpVtbuPrw9FHngYvP0b+vr7J21J\nQgghRDYSCzOAfftCkfDWt4bbL3sZfO97YeOnV76y9vcrBBFGjZ++A3RH36m5khBCTD0kFqYxX/1q\nKBIuuyzcftWr4Lnn4Nln4eyzx/fe8SDC3dH+sXa9FEIIMfFILExDvvzlUCT86q+G28uWwfBwGJdw\n5pkTMwa3vXRfXx/5fL7M29BDlLLY2am4ASGEqGOUDTGN6OmBbduK2xddBI88AgsXTt6Y3AJFPXv3\nsnXzZra5tReUsiiEEHWPxMI04C//Eq66qrj9xjfCQw/B/PmTNyYfSlkUQoipicTCFOb22+Haa4vb\nb3kL9PXB6adP2pAqQuWQhRBiaqGYhSnIpz8dxiRYoXDZZfDCC/C1r9W/UMhisqs7CiGEKEdiYQrx\nZ38WioQbbwy33/nOsEZCXx/MnTu5Yxsrw8PDXL5pE8uWLaOrq4ulS5dy+aZNnDp1arKHJoQQMx6J\nhTrHGPjTPw1Fwh//cbhv82Z48UX4m78Jqy9OB9J6SQghhJhcFLNQpxgDO3aESw6W970PuruhoWHy\nxjUeJPWSMCMjbIuqOyrGQQghJg95FuoMY8JYhFyuKBR++7dhZAQ+97npJxQguZeEqjsKIUR9ILFQ\nJ7z0EnzgA6FIuP32cN/v/V64/447wv3TFV8vCVB1RyGEqBemsQmaGoyMwNatocfgnnvCfX/0R6FI\n2L07jFWY7vh6Sai6oxBC1A8SC5PEiy/Cr/0azJoFX/xiuO/jHw+XIW6+eWaIBJd4L4ltwBpVdxRC\niLpgVGIhCIJrgiD4dhAEPw2C4HAQBKtTjj07CIIvBkEwGATBSBAEfz764U4PvvIVmDMnzGYA+NSn\nQpHwJ38yueOaTHy9JB7Yt4/m5ubJHpoQQsx4qs6GCILgPcCngauBI8ANQH8QBEuNMc97TpkL/Cdw\nU3TsjOf73w9/3nYbXHfd5I6l3lB1RyGEqD9G41m4AbjbGPNXxpj/C3wQ+G/g/b6DjTH/Zoy5wRjT\nA/xo9EOdPlx7behJkFAQQggxFahKLARBMBtYCTxk9xljDHAAeFNthyaEEEKIeqBaz8KZQAPwbGz/\ns8DZNRmREEIIIeoKZUMIIYQQIpVqAxyfB0aAxbH9i4Hv12REDjfccAONjY0l+zZv3sxm9QsQQggh\n2Lt3L3tjKeY//OEPa36fIAw5qOKEIDgMPGaMuT7aDgh7/9xmjNmVce7XgG8YY34347g24OjRo0dp\na2uranxCCCHETObxxx9n5cqVACuNMY/X4pqjaST158AXgiA4SjF1ch7wBYAgCD4BvMIY8157QhAE\nFwEBsAA4K9r+uTHmqbENXwghhBDjTdViwRjzpSAIzgQ+Trj88ATQaYx5LjrkbOCc2GnfAKwLow3Y\nAvwbcN5oBi2EEEKIiWNULaqNMXcAdyR89z7PPgVSOuTzeU6cOEFra6sKEAkhhKh7ZMQnkOHhYS7f\ntIlly5bR1dXF0qVLuXzTJk6dOjXZQxNCCCESkViYQLZt2cLhAwfoIYwI7QEOHzjAVmV3CCGEqGNG\ntQwhqiefz9PX308PcEW07wrAjIywrb+fY8eOaUlCCCFEXSLPwgRx4sQJANbH9rdHP48fPz6h4xFC\nCCEqRWJhgjj//PMBeCS2/1D0s7W1dULHI4QQQlSKxMIEsXTpUro6O9ne0EAP8B3CmIXrGxro6uzU\nEoQQQoi6RWJhAunZu5c1GzawDTgX2Aas2bCBnlipTiGEEKKeUIDjBNLc3MwD+/Zx7Ngxjh8/rjoL\nQgghpgQSC5PAkiVLJBKEEEJMGbQMIYQQQohUJBaEEEIIkYrEghBCCCFSkVgQQgghRCoSC0IIIYRI\nRWJBCCGEEKlILAghhBAiFYkFIYQQQqQisSCEEEKIVCQWhBBCCJGKxIIQQgghUpFYEEIIIUQqEgtC\nCCGESEViQQghhBCpSCwIIYQQIhWJBSGEEEKkIrEghBBCiFQkFoQQQgiRisSCEEIIIVKRWBBCCCFE\nKhILQgghhEhFYkEIIYQQqUgsCPH/t3f/sVfVdRzHn68ZRrAxUur71WJSYmCtISszKn4sLWcu3KrZ\nygWbzTRzlVvh6o9ybsVo/bLy13KQlbJYq+YaJhn1RwVYoNkKkEJWqZAigwZS9uXTH5/DOlzuOd97\nzveeez/38npsd7v38Pmc+3nzvufc9/f8uB8zMyvlYsHMzMxKuVgwMzOzUi4WzMzMrJSLBTMzMyvl\nYsHMzMxKuVgwMzOzUi4WzMzMrJSLBTMzMyvlYqEH1q5d2+8hdJXjSdcwxQKOJ2XDFAsMXzzdVqtY\nkPQxSU9Iel7SZkkXjtN+iaStko5KelzS8nrDHUzD9iF0POkapljA8aRsmGKB4Yun2yoXC5LeD3wF\n+DwwH/gD8KCkGQXtZwE/BX4BzANuBe6W9I56QzYzM7NeqnNk4UbgrhDCd0MIO4DrgCPA1QXtPwrs\nDiGsCCHsDCHcBvwwW4+ZmZklrlKxIGkS8AbiUQIAQggBeAhYUNDtzdm/5z1Y0t7MzMwS8qKK7WcA\npwH7WpbvA+YU9BktaD9N0otDCP9u02cywPbt2ysOL00HDx5k27Zt/R5G1ziedA1TLOB4UjZMscBw\nxZP77pzcrXUqHhjosLF0FvAksCCEsCW3fBWwKIRw0tECSTuB1SGEVblllxGvY5jSrliQ9EHg3iqB\nmJmZ2QmuCiHc140VVT2y8CwwBoy0LB8B9hb02VvQ/lDBUQWIpymuAvYARyuO0czM7FQ2GZhF/C7t\nikrFQgjhBUlbgYuB+wEkKXv9jYJum4DLWpa9M1te9D77ga5UQ2ZmZqeg33ZzZXXuhvgqcI2kZZLm\nAncCU4DvAEhaKemeXPs7gVdLWiVpjqTrgfdl6zEzM7PEVT0NQQhhXfabCrcQTyc8ClwaQngmazIK\nzMy13yPpcuBrwMeBfwAfDiG03iFhZmZmCap0gaOZmZmdejw3hJmZmZVysWBmZmal+l4sSDpH0t2S\ndks6ImmXpJuzX4ss67dG0rGWx/pejbtkXLXiyfreIumprN/PJc3uxZjHI+mzkn4j6bCk5zrsk2p+\nKseS9Us1Ny+VdK+kg5IOZJ+9qeP0SSY3wzQpXZVYJC1uk4MxSS/v5ZiLSFoo6X5JT2ZjW9pBn5Rz\nUymelPMj6TOSHpZ0SNI+ST+W9JoO+k0oP30vFoC5gIBrgNcS54y4DvhCB30fIF5kOZo9PtDQGKuo\nFY+km4AbgI8AbwIOEyfoOr3R0XZmErAOuKNivxTzUzmWxHNzH3A+8fbly4FFwF0d9Ot7bjREk9JV\njSUTgPP4fw7OCiH8s+mxdmgq8eL164njLJVybjKV4smkmp+FwDeBi4BLiPu0DZJeUtShK/kJIST3\nAD4F/GWcNmuAH/V7rF2M5yngxtzracDzwJX9Hn9uTMuB5zpsm3R+KsaSZG6IhekxYH5u2aXAf4HR\n1HMDbAZuzb0W8W6pFQXtVwGPtSxbC6wfwFgWE3/gblq/x95BbMeApeO0STY3NeMZpPzMyGJ6W5P5\nSeHIQjvTgU4OES/JDsPskHS7pDOaHlhNpfFIehWxcs1P0HUI2MJgT7g1KPkplHhuFgAHQgiP5JY9\nRPyL6KJx+vY1NxqiSelqxgKxoHg0O721QdJbmh1po5LMzQQNSn6mE7f5su/MCecnuWIhOxd8A/HH\nnMo8ACwD3g6sIFaC6yWp2RFW02E8o8Rkt5twa7ShoTVtIPLTgZRzMwqccFg0hDBG3GmUjS2F3JRN\nSlc09tJJ6bo7vErqxPI0cC3wXuA9wN+BX0m6oKlBNizV3NQ1EPnJttmvA78OIfy5pOmE81P5R5k6\nJWklcFNJkwCcH0J4PNfnFcQd2Q9CCKvL1h9CWJd7+SdJfwT+CiwBfll33EWajqfX6sRTRS/z03Qs\nvdZpPHXX3+ttx06WfRbzn8fNks4lXuOUzIWBp6oBys/txGvj3tr0GzVWLABfJp4bLbP7+BNJZwMb\niRXStVXfLITwhKRngdk0s8NrMp69xENeI5xY/Y0Aj7TtMXGV4pmohvPTZCwp52YvcMLV2ZJOA86g\neGK3k/Rg22mnV5PS9UKdWNp5mB7s9BuSam66Kan8SPoW8C5gYQjh6XGaTzg/jRULIU4Gtb+Tttlf\n4BuB3wFX13k/Sa8EziQePuq6JuPJdtZ7iVe0P5atYxrxvPNtdcc8znt2HE83NJmfJmNJOTeSNgHT\nJc3PXbdwMbG42VLc86T1NLrttBN6NCldL9SMpZ0L6GEOuizJ3HRZMvnJCoUrgMUhhL910GXi+Ung\nSs6zgV3Ahuz5yPFHS7sdwBXZ86nAl4g77HOIG+Xvge3ApEGLJ3u9gvgF8W7g9cBPsvWcnkCOZhJv\nt/kccDB7Pg+YOoD5qRTLAORmffZ/eyHxr56dwPeKPmsp5Qa4EjhCvH5iLvGWz/3Ay7J/Xwnck2s/\nC/gX8cruOcTb4P4DXJJAHqrG8glgKXAu8DrieecXgCX9jiX3OZlH/II8Bnwyez1z0HJTM55k80M8\n9XCAeAvlSO4xOdfmi93OTwpJXE48hJd/HAPGWtqNAcuy55OBnxEPrRwlHpK94/iGOWjx5JbdTLxN\n7wjxStXZ/Y4nG9eaNjGNAYsGMD+VYhmA3EwHvk8sfA4A3wamFH3WUstNttPaQ7wVdRPwxpZcbWxp\nvwjYmrXfBXyo3zmoEwvw6Wz8h4FniHdSLOr1mEtiWXx8v9XyWD2guakUT8r5KYjjhH1WE/nxRFJm\nZmZWKrlbJ83MzCwtLhbMzMyslIsFMzMzK+ViwczMzEq5WDAzM7NSLhbMzMyslIsFMzMzK+ViwczM\nzEq5WDAzM7NSLhbMzMyslIsFMzMzK/U/DAltZigwxHcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2141f1c66d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_data,y_data,c='r')\n",
    "plt.plot(x_data,sess.run(W)*x_data+sess.run(b))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['', 'C:\\\\Anaconda3\\\\python35.zip', 'C:\\\\Anaconda3\\\\DLLs', 'C:\\\\Anaconda3\\\\lib', 'C:\\\\Anaconda3', 'C:\\\\Anaconda3\\\\lib\\\\site-packages', 'C:\\\\Anaconda3\\\\lib\\\\site-packages\\\\Sphinx-1.4.6-py3.5.egg', 'C:\\\\Anaconda3\\\\lib\\\\site-packages\\\\protobuf-3.1.0.post1-py3.5.egg', 'C:\\\\Anaconda3\\\\lib\\\\site-packages\\\\win32', 'C:\\\\Anaconda3\\\\lib\\\\site-packages\\\\win32\\\\lib', 'C:\\\\Anaconda3\\\\lib\\\\site-packages\\\\Pythonwin', 'C:\\\\Anaconda3\\\\lib\\\\site-packages\\\\setuptools-27.2.0-py3.5.egg', 'C:\\\\Anaconda3\\\\lib\\\\site-packages\\\\IPython\\\\extensions', 'C:\\\\Users\\\\user\\\\.ipython']\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "print (sys.path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
